Hybrid Beamforming Grouping Sum-Rate Maximization Algorithm for Multiuser mmWave Massive MIMO Systems

Abstract

For multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the key factor that causes the system sum-rate to change is its interference plus noise, corresponding to the number of base station (BS) antennas and signal-to-noise ratio (SNR) variation causing the system sum-rate changes, which in turn affects the user’s communication quality. Based on this, this paper proposes an improved low-complexity hybrid beamforming grouping sum-rate maximization (HBG-SRM) algorithm to achieve system sum-rate maximization under the premise that both BS and user side use hybrid beamforming architecture and the channel state information (CSI) of the downlink channel is perfect. The algorithm first predefines a relevant threshold value, which is used to group multiusers; then, the user uses the maximum likelihood (ML) criterion to identify the optimal beam and estimate its beamforming gain within each group, and finally, the user compares all the candidate optimal beam gains between each group to confirm the optimal beamforming vector. The simulation results also verify the superiority of the proposed algorithm’s sum-rate over other algorithms.